Matlab Download Deep Learning Toolbox

Deep Learning Toolbox provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. The Experiment Manager app helps you manage multiple deep learning experiments, keep track of training parameters, analyze results, and compare code from different experiments. You can visualize layer activations and graphically monitor training progress.

Speed up the development of deep learning models using low-code apps. Create, train, analyze, and debug a network using Deep Network Designer app. Tune and compare multiple models using Experiment Manager app.

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Automatically generate optimized CUDA code with GPU Coder, and generate C and C++ code with MATLAB Coder to deploy deep learning networks to NVIDIA GPUs and various processors. Prototype and implement deep learning networks on FPGAs and SoCs using Deep Learning HDL Toolbox.

Quantize and prune your deep learning network to reduce memory usage and increase inference performance. Analyze and visualize the tradeoff between increased performance and inference accuracy using the Deep Network Quantizer app.

Deep Learning Onramp

This free, two-hour deep learning tutorial provides an interactive introduction to practical deep learning methods. You will learn to use deep learning techniques in MATLAB for image recognition.

Interactively Modify a Deep Learning Network for Transfer Learning

Deep Network Designer is a point-and-click tool for creating or modifying deep neural networks. This video shows how to use the app in a transfer learning workflow. It demonstrates the ease with which you can use the tool to modify the last few layers in the imported network as opposed to modifying the layers in the command line. You can check the modified architecture for errors in connections and property assignments using a network analyzer.

Deep Learning with MATLAB: Transfer Learning in 10 Lines of MATLAB Code

Learn how to use transfer learning in MATLAB to re-train deep learning networks created by experts for your own data or task.

Sivylla is a Product Marketing Manager for AI, who previously researched and interfaced biological neural networks. She writes about machine learning and deep learning methods, how and when to apply these methods, and the interoperability between MATLAB, TensorFlow, and PyTorch.

The R2017b release of MathWorks products shipped just two weeks ago, and it includes many new capabilities for deep learning. Developers on several product teams have been working hard on these capabilities, and everybody is excited to see them make it into your hands. Today, I'll give you a little tour of what you can expect when you get a chance to update to the new release.

The heart of deep learning for MATLAB is, of course, the Neural Network Toolbox. The Neural Network Toolbox introduced two new types of networks that you can build and train and apply: directed acyclic graph (DAG) networks, and long short-term memory (LSTM) networks.

If you are implementing deep learning methods in embedded system, take a look at GPU Coder, a brand new product in the R2017b release. GPU Coder generates CUDA from MATLAB code for deep learning, embedded vision, and autonomous systems. The generated code is well optimized, as you can see from this performance benchmark plot.

Learn the basics of deep learning for image classification problems in MATLAB. Use a deep neural network that experts have trained and customize the network to group your images into predefined categories.

Many pretrained networks are available in Deep Learning Toolbox. For more information, see Pretrained Deep Neural Networks. However, MATLAB does not stand alone in the deep learning ecosystem. Use the import and export functions to access models available in open-source repositories and collaborate with colleagues who work in other deep learning frameworks.

Preprocessing data is a common first step in the deep learning workflow to prepare data in a format that the network can accept. The input data size must match the network input size. If the sizes do not match, you must resize the input data. For an example, see Import ONNX Network as DAGNetwork. In some cases, the input data requires further processing, such as normalization. For an example, see Import ONNX Network with Automatically Generated Custom Layers.

Transfer learning is common in deep learning applications. You can use a pretrained network as a starting point to learn a new task. Fine-tuning a network with transfer learning is usually much faster and easier than training a network with randomly initialized weights from scratch. You can quickly transfer learned features to a new task using a smaller quantity of training data. This section describes how to import a convolutional model from TensorFlow for transfer learning.

When you complete your deep learning workflows in MATLAB, you can share the deep learning network or layer graph with colleagues who work in different deep learning platforms. By entering one line of code, you can export the network. e24fc04721